Databricks Exam Databricks-Machine-Learning-Professional Topic 6 Question 28 Discussion

Actual exam question for Databricks's Databricks-Machine-Learning-Professional exam

Question #: 28
Topic #: 6

[All Databricks-Machine-Learning-Professional Questions]

Which of the following Databricks-managed MLflow capabilities is a centralized model store?

AModels

BModel Registry

CModel Serving

DFeature Store

EExperiments

Show Suggested Answer

Suggested Answer: C

by Cecily at Sep 17, 2024, 11:47 AM

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Christiane

17 days ago

The real question is, which one of these options is the best for making my boss look good? I'll go with 'All of the above!'

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Dacia

18 days ago

I bet the answer is 'D) Feature Store' - that's where you keep all your special ML superpowers!

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Jerry

19 days ago

B) Model Registry, for sure. That's where you store and version your trained models.

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Marylou

21 days ago

E) Experiments sounds like the right answer. Isn't that where you track your model training runs?

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Tatum

2 days ago

Actually, it is Model Registry. That's where you manage and version your models.

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Annice

5 days ago

I think Model Registry is the centralized model store.

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Rolande

6 days ago

Yes, you are correct. Experiments is where you track your model training runs.

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Slyvia

25 days ago

Hmm, I think it's C) Model Serving. Isn't that where you deploy your models?

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Keshia

27 days ago

B) Model Registry is definitely the centralized model store. I'm sure of this!

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Jill

4 days ago

C) Model Serving is also crucial for deploying models.

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Jessenia

8 days ago

A) Models are important too, but Model Registry is where it's at.

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Carmela

10 days ago

B) Model Registry is indeed the centralized model store.

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Jerry

1 months ago

B. Model Registry. Duh! I mean, where else would you keep your models? The fridge?

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Pok

1 months ago

Model Registry all the way! Keeping my models in a centralized location is a must for any serious machine learning project.

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Mee

7 days ago

I think Feature Store is important for storing and managing features used in machine learning models.

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Aleta

18 days ago

I prefer Model Serving for deploying and serving models in production.

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Sang

21 days ago

I agree, Model Registry is essential for managing and organizing models.

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Delsie

1 months ago

Hmm, I'm torn between B and D. Feature Store could also be considered a centralized model store, but I think Model Registry is the better fit.

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Kathrine

1 days ago

Let's go with Model Registry then.

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Mona

2 days ago

Feature Store could also work, but Model Registry seems more centralized.

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Leonie

7 days ago

I agree, Model Registry is the best fit for that.

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Alpha

24 days ago

I think Model Registry is the centralized model store.

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Roxane

2 months ago

I'm not sure, but I think Model Serving could also be a centralized model store.

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Truman

2 months ago

I'm going with B. Model Registry. Gotta keep those models organized and accessible, you know?

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Heidy

19 days ago

I always make sure to use Model Registry for easy access to my models.

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Thaddeus

25 days ago

I think Model Registry is essential for managing models efficiently.

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Mirta

1 months ago

It's so convenient to have all the models in one centralized location.

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Florinda

1 months ago

I agree, Model Registry is definitely the way to go.

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Rodrigo

2 months ago

I agree with Lyda, Model Registry is where you can store all the models.

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Lyda

2 months ago

I think the centralized model store is Model Registry.

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Vilma

2 months ago

The Model Registry seems like the obvious choice here. It's a centralized place to store and manage machine learning models, right?

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Una

1 months ago

I agree, the Model Registry is where you can store and manage your machine learning models in a centralized way.

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Roslyn

2 months ago

Yes, you are correct. The Model Registry is indeed the centralized model store for Databricks-managed MLflow.

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